Metallic aerospace components are commonly painted with a primer to improve their corrosion resistance. The primer contains a polymer matrix with embedded corrosion inhibitor and filler particles. Its performance is determined by the microscopic distributions of the particles. Various techniques have been used to quantify such distributions, including X-ray micro-computed tomography (CT). However, its success is sometimes limited by factors such as different particles having similar X-ray CT absorption properties and their size being smaller than the resolution of micro-CT. In this paper, we have performed two X-ray CT measurements on a paint primer sample consisting of SrCrO4 corrosion inhibitor particles and UV-absorbing TiO2 filler particles. Fe and Ti targets were used as X-ray sources with different spectral distributions. The measured CT data sets were used as constraints for a data-constrained microstructure modeling (DCM) prediction of the sample’s microscopic structures. DCM model predictions were compared with experimental elemental surface maps and showed reasonable degree of agreement, suggesting X-ray micro-CT combined with DCM modeling would be a powerful technique for detailing the dynamics of chromate-inhibited primers and other multiphase systems where the components are sensitive to incident X-ray energy.